Deep learning is a type of machine learning that attempts to model high-level abstractions in data by using multiple processing layers or multiple non-linear transformations. Deep learning uses representations of data, typically in vector format, where each datum corresponds to an observation with a known outcome. By processing over many observations with known outcomes, deep learning allows for a model to be developed that can be applied to a new observation for which the outcome is not known.
Some deep learning techniques are based on interpretations of information processing and communication patterns within nervous systems. One example is an artificial neural network. Artificial neural networks are a family of deep learning models based on biological neural networks. They are used to estimate functions that depend on a large number of inputs where the inputs are unknown. In a classic presentation, artificial neural networks are a system of interconnected nodes, called neurons, that exchange messages via connections, called synapses between the neurons.
An example classic artificial neural network system can be represented in three layers: the input layer, the hidden layer, and the output layer. Each layer contains a set of neurons. Each neuron of the input layer is connected via numerically weighted synapses to nodes of the hidden layer, and each neuron of the hidden layer is connected to the neurons of the output layer by weighted synapses. Each neuron has an associated activation function that specifies whether the neuron is activated based on the stimulation it receives from its inputs synapses.
An artificial neural network learns using examples. The learning process can be referred to as training the neural network. During training, a data set of known inputs with known outputs is collected. Inputs to the artificial neural network are typically vectors that are applied to the input layer of the network. Based on some combination of the value of the activation function for each input neuron, the sum of the weights of synapses connecting input neurons to neurons in the hidden layer, and the activation function of the neurons in the hidden layer, some neurons in the hidden layer will activate. This, in turn, will activate some of the neurons in the output layer based on the weight of synapses connecting the hidden layer neurons to the output neurons and the activation functions of the output neurons. The activation of the output neurons is the output of the network, and this output, like the input, is typically represented as a vector. Learning occurs by comparing the output generated by the network for a given input to that input's known output. Using the difference between the output produced by the network and the expected output, the weights of synapses are modified starting from the output side of the network and working toward the input side of the network. Once the difference between the output produced by the network is sufficiently close to the expected output (defined by the cost function of the network), the network is said to be trained to solve a particular problem. While the above example explains the concept of artificial neural networks using one hidden layer, some artificial neural networks include several hidden layers.